Event Pattern Detection for Energy Management
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Solution Overview
Problem
Dynamic client facilities with changing environmental conditions often lead to incorrect configuration and equipment failure in energy management platforms, necessitating a system that can automatically detect and prioritize repairs across multiple sites to optimize maintenance and reduce expenses.
Innovation Solution
An energy management platform that collects and analyzes performance data, using rules to detect patterns and create events, which are then ranked for priority, with successive passes identifying higher-level issues and utilizing computerized visualization to display temporal patterns for effective problem diagnosis and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If an energy management platform is managed at multiple client facilities, then the ability to detect and prioritize problems improves, but the complexity of managing and coordinating repairs across facilities increases
Solution Approach 1:
The system segments the management of multiple client facilities by creating facility-specific event patterns and repair priority assignments. Each facility is treated as an independent unit with its own detected problems and repair schedules, allowing centralized oversight without requiring complex coordination between facilities. The segmentation enables scalable management where each facility's issues are handled independently through standardized processes.
2Ease of operation
If manual monitoring and repair prioritization is used across multiple facilities, then flexibility in handling unique cases is maintained, but time and resources are wasted on manual analysis and ranking of repair priorities
Solution Approach 1:
The system implements self-service through automated event pattern detection and repair priority assignment. The platform automatically monitors facilities, detects equipment failures and anomalies, generates repair work orders, and prioritizes them based on predefined criteria such as equipment criticality and failure impact. This eliminates the need for manual analysis while maintaining operational flexibility through configurable detection parameters and priority rules that can be adjusted without requiring manual intervention for each incident.
Data Source
AI summary
Data relating to energy management may be collected and stored from one or more sites. This data may be analyzed by a series of rules, and each rule may look for certain patterns in the data over time. Each time a pattern is detected in the data, the rule may create and store an event back into the database. Each event may represent the detection of a specific condition that starts at a specific time and continues for a specific duration. Each rule may also dynamically assign and update a score to each event that indicates its level of importance and persistence. Once the raw data has been analyzed and events have been created that represent basic conditions, a new set of rules may analyze the events themselves, in addition to, or rather than, the raw data. Successive passes of rules can thus detect higher level, broader problems.